class_docstr: A posterior based on GPyTorch's multi-variate Normal distributions.
docstr: The shape of a base sample used for constructing posterior samples.
docstr_tok: ["The","shape","of","a","base","sample","used","for","constructing","posterior","samples","."]
code: def base_sample_shape(self) -> torch.Size:
      r"""The shape of a base sample used for constructing posterior samples."""
      return (
          self.distribution.batch_shape
          + self.distribution.base_sample_shape
      )
  
imports: from __future__ import annotations
  import warnings
  from contextlib import ExitStack
  from typing import Optional, Tuple, TYPE_CHECKING, Union
  import torch
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.posteriors.base_samples import _reshape_base_samples_non_interleaved
  from botorch.posteriors.torch import TorchPosterior
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from linear_operator import settings
  from linear_operator.operators import BlockDiagLinearOperator, DenseLinearOperator, LinearOperator, SumLinearOperator
  from torch import Tensor
  from torch.distributions import Normal
label_desc: Use image node_id 3 for calling the GPyTorchPosterior obj's underlying member method code with example usage: obj.base_sample_shape() and returns: unknown